Knowledge Discovery for HR Turnover Risk and Financial Exposure

Abstract

Employee turnover creates financial and operational challenges through recruiting, onboarding, training, productivity loss, and reduced workforce stability. This project applied a simplified Knowledge Discovery approach to estimate employee turnover risk and financial exposure using workforce indicators. A synthetic dataset of 15 employee records was created to simulate department, monthly salary, overtime status, job satisfaction, and years at company. Excel was used to transform the dataset by calculating turnover risk score, turnover risk class, retention priority segment, annual salary, and estimated financial exposure. Power BI was then used to create a basic data model, data analysis expressions measures, and an interactive dashboard. The results identified high-risk employee groups, department-level financial exposure, and workforce risk patterns related to overtime, job satisfaction, and tenure. The project demonstrates how simple workforce data can be transformed into decision-support insights for human resources planning and retention strategies.